
Knowledge inspired representation and interaction modeling for more accurate and explainable prediction. Inspired by the knowledge that DTI is sub-structural, MolTrans  derives a data-driven method called Frequent Consecutive Sub-sequence (FCS) mining that is adaptable to extract high-quality fit-sized sub-structures for both protein and drug. In addition, MolTrans includes a bio-inspired interaction module imitating the real biological DTI process. The new sub-structure fingerprints enable a tractable path for understanding which sub-structure combination has more relevance to the outcome through an explicit map in the interaction module.

Leverage massive unlabelled biomedical data.
MolTrans mines through millions of drugs and proteins sequences from multiple unlabelled data sources to extract high quality sub-structures of drugs and proteins. The vast data result in a much higher quality sub-structures than using small training dataset alone. We also augment the representation using transformers [1]}, which captures the complex signals among the large sequential sub-structures outputs generated from the unlabelled data.

